ViStoryBench: Comprehensive Benchmark Suite for Story Visualization

Story visualization aims to generate coherent image sequences that faithfully depict a narrative and align with character references. Despite progress in generative models, existing benchmarks are narrow in scope, often limited to short prompts, lacking character reference, or single-image cases, and fail to capture real-world storytelling complexity. This hinders a nuanced understanding of model capabilities and limitations. We present \textbf{ViStoryBench}, a comprehensive benchmark designed to evaluate story visualization models across diverse narrative structures, visual styles, and character settings. The benchmark features richly annotated multi-shot scripts derived from curated stories spanning literature, film, and folklore. Large language models assist in story summarization and script generation, with all outputs verified by humans to ensure coherence and fidelity. Character references are carefully curated to maintain intra-story consistency across varying artistic styles. To enable thorough evaluation, ViStoryBench introduces a set of automated metrics that assess character consistency, style similarity, prompt alignment, aesthetic quality, and generation artifacts such as copy-paste behavior. These metrics are validated through human studies, and are used to benchmark a broad range of open-source and commercial models. ViStoryBench offers a multi-dimensional evaluation suite that facilitates systematic analysis and fosters future progress in visual storytelling.